464 research outputs found
Multimodal Grounding for Language Processing
This survey discusses how recent developments in multimodal processing
facilitate conceptual grounding of language. We categorize the information flow
in multimodal processing with respect to cognitive models of human information
processing and analyze different methods for combining multimodal
representations. Based on this methodological inventory, we discuss the benefit
of multimodal grounding for a variety of language processing tasks and the
challenges that arise. We particularly focus on multimodal grounding of verbs
which play a crucial role for the compositional power of language.Comment: The paper has been published in the Proceedings of the 27 Conference
of Computational Linguistics. Please refer to this version for citations:
https://www.aclweb.org/anthology/papers/C/C18/C18-1197
Multimodal music information processing and retrieval: survey and future challenges
Towards improving the performance in various music information processing
tasks, recent studies exploit different modalities able to capture diverse
aspects of music. Such modalities include audio recordings, symbolic music
scores, mid-level representations, motion, and gestural data, video recordings,
editorial or cultural tags, lyrics and album cover arts. This paper critically
reviews the various approaches adopted in Music Information Processing and
Retrieval and highlights how multimodal algorithms can help Music Computing
applications. First, we categorize the related literature based on the
application they address. Subsequently, we analyze existing information fusion
approaches, and we conclude with the set of challenges that Music Information
Retrieval and Sound and Music Computing research communities should focus in
the next years
Alzheimer's Dementia Recognition Using Acoustic, Lexical, Disfluency and Speech Pause Features Robust to Noisy Inputs
INTERSPEECH 2021. arXiv admin note: substantial text overlap with arXiv:2106.09668INTERSPEECH 2021. arXiv admin note: substantial text overlap with arXiv:2106.09668INTERSPEECH 2021. arXiv admin note: substantial text overlap with arXiv:2106.09668We present two multimodal fusion-based deep learning models that consume ASR transcribed speech and acoustic data simultaneously to classify whether a speaker in a structured diagnostic task has Alzheimer's Disease and to what degree, evaluating the ADReSSo challenge 2021 data. Our best model, a BiLSTM with highway layers using words, word probabilities, disfluency features, pause information, and a variety of acoustic features, achieves an accuracy of 84% and RSME error prediction of 4.26 on MMSE cognitive scores. While predicting cognitive decline is more challenging, our models show improvement using the multimodal approach and word probabilities, disfluency and pause information over word-only models. We show considerable gains for AD classification using multimodal fusion and gating, which can effectively deal with noisy inputs from acoustic features and ASR hypotheses
Alzheimer's Dementia Recognition Using Acoustic, Lexical, Disfluency and Speech Pause Features Robust to Noisy Inputs
We present two multimodal fusion-based deep learning models that consume ASR
transcribed speech and acoustic data simultaneously to classify whether a
speaker in a structured diagnostic task has Alzheimer's Disease and to what
degree, evaluating the ADReSSo challenge 2021 data. Our best model, a BiLSTM
with highway layers using words, word probabilities, disfluency features, pause
information, and a variety of acoustic features, achieves an accuracy of 84%
and RSME error prediction of 4.26 on MMSE cognitive scores. While predicting
cognitive decline is more challenging, our models show improvement using the
multimodal approach and word probabilities, disfluency and pause information
over word-only models. We show considerable gains for AD classification using
multimodal fusion and gating, which can effectively deal with noisy inputs from
acoustic features and ASR hypotheses.Comment: INTERSPEECH 2021. arXiv admin note: substantial text overlap with
arXiv:2106.0966
Visually Grounded Meaning Representations
In this paper we address the problem of grounding distributional representations of lexical meaning. We introduce a new
model which uses stacked autoencoders to learn higher-level representations from textual and visual input. The visual modality is
encoded via vectors of attributes obtained automatically from images. We create a new large-scale taxonomy of 600 visual attributes
representing more than 500 concepts and 700K images. We use this dataset to train attribute classifiers and integrate their predictions
with text-based distributional models of word meaning. We evaluate our model on its ability to simulate word similarity judgments and
concept categorization. On both tasks, our model yields a better fit to behavioral data compared to baselines and related models which
either rely on a single modality or do not make use of attribute-based input
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